Exploratory analytics or exploratory data analysis is a data analysis approach in which hypotheses worth testing are formulated, and which complements the tools of conventional statistics for testing hypotheses. Statistician John Tukey named exploratory data analysis to contrast with Confirmatory Data Analysis, which is the term used for the set of ideas about hypothesis testing, p-values, confidence intervals, and so on. In a sense, exploratory analytics is the process of learning what you need to ask. Exploratory analytics is especially relevant today with the explosion of diverse types of information both within organizations and in the public domain. While standard search and text mining algorithms have proven invaluable in accessing such data, difficulties still arise when users have insufficient knowledge of the data to know what to search for. In this regard, “search” is like “confirmatory data analysis” which requires a hypothesis (i.e., a search term) in order to begin. In many cases it is wise not to form such a hypothesis immediately, but to let the data guide the formulation of a hypothesis through analytics. In particular, Intellectual Properties (IP) is an excellent domain to prove the value of exploratory data analysis. First of all, IP is one of the most valuable information assets to corporations. Appropriate management and leverage of IP information can create significant competitive advantages, generate high-value and low-cost returns through licensing and divesting opportunities, and enable major science and technology breakthroughs. IP activities may range from prior art search, portfolio analysis and management, licensing target identification, divestiture analysis, to patent valuation. As the patent portfolio grows, knowledge discovery in patent data becomes more valuable, as it can save significant money for enterprises in their patent management cost.